Abstract

First-order, second-order, sigmoid, and absorption models were used to predict the extraction yield (EY) of anise essential oil (AEO). Sigmoid model was able to predict the EY with R2 0.9852 and 0.9869 respectively for ohmic-assisted hydro-distillation methods with ultrasound and microwave pretreatments. It was found that among the different prediction models, adaptive neuro-fuzzy inference system (ANFIS) has the highest accuracy for EY estimating. So that a network with the help of Mamdani's maximum-minimum method and triangular membership function was able to predict the EY in both pretreatments with R2 above 0.99. The results of artificial neural network (ANN) modeling showed that the multilayer perceptron network with feed-forward back propagation structure, Levenberg–Marquardt training algorithm, and with configurations 3-16-7-1 and 3-14-5-1 was able to have the best performance in EY predicting of ultrasound and microwave pretreatments, respectively. The results emphasize the successful use of models, especially dynamic models, in predicting the kinetics of essential oil extraction.